Usage

Basic Usage

Import the gm module of PyEst as well as numpy and matplotlib:

import numpy as np
import matplotlib.pyplot as plt
import pyest.gm as gm

Create a three-mixand two-dimensional Gaussian mixture:

# mixand means (nc,nx)
m = np.array([[0,0], [1,2], [0,-1]])
# mixand covariance matrices (nc,nx,nx)
P = np.array([[[1,0], [0,1]],
              [[2, 0.5], [0.5,3]],
              [[0.5, -0.1], [-0.1, 1]]])
# mixand weights (nc,)
w = gm.equal_weights(3)
# contruct the Gaussian mixture
p = gm.GaussianMixture(w, m, P)

Compute the mean and covariance of the distribution:

# compute and print the mean
print(p.mean())
# compute and print the covariance
print(p.cov())

Plot the Gaussian mixture:

pp, XX, YY = p.pdf_2d()
fig = plt.figure()
ax = fig.add_axes(111)
ax.contourf(XX,YY,pp,100)

Apply a linear transformation to the mixture:

dt = 5
F = np.array([[1, dt], [0, 1]])
my = np.array([F@m for m in p.m])
Py = np.array([F@P@F.T for P in p.P])
py = gm.GaussianMixture(p.w, my, Py)

Plot the transformed Gaussian mixture:

pp, XX, YY = py.pdf_2d()
fig = plt.figure()
ax = fig.add_axes(111)
ax.contourf(XX,YY,pp,100)
plt.show()

Advanced Usage

For more advanced usage, including nonlinear transformations and splitting methods, please refer to the Examples